Tuesday, April 5, 2022

Crypto Twitter

With Elon Musk joining the board of Twitter, now is a good time to consider all the things Twitter should be doing, that it has never done. There are lots of obvious features (edit tweets, longer form content, better user controls, better onboarding, better spam filtering, business account support and integrated CRM and CS, etc) that were being debated within Twitter even when I was a VP at Twitter 10 years ago! 

So instead of focusing on all those things, I thought it would be interesting to think about moving Twitter from the web 2 into a web 2/3 hybrid. This could open up significant user benefit, as well as monetization potential for Twitter. If a Twitter buy-out were to ever occur, these might be interesting mechanisms to increase the value of the company substantially.

Potential parts of crypto Twitter [1]:

NFTs. 

  • This is an obvious one - people on Twitter should be able to mint and distribute NFTs via Twitter. Imagine OpenSea as a deep Twitter integration. As you purchase an NFT on the platform you could be prompted to swap this in for your profile image.
  • Distribution of NFTs could occur via the platform with for example content creators providing priority access to their followers.
Tokens.
  • Twitter could do "Bitclout the right way". For example, each person could have tokens issues for them that could be purchased, distributed, and used for a variety of in app use cases including purchasing of NFTs (see above) or participating in some future on platform revenue generated by that individual.
  • Token or NFT ownership could be used to allow entry into specific entry or interest groups, a la Farcaster. This could be external token buys (e.g. buy UNI to get into a Uniswap specific group) or internal (buy Billie Eilish tokens or NFT to get into her special community or fan group).
Crypto Identity and Wallet.
  • Each user could have a wallet and related identity auto-generated as part of a Twitter account. This identity could be used in interesting cross platform ways ("cryptoauth with Twitter") around the web. The wallet could be used to hold crypto assets (including tokens from the platform and NFTs) as well as be used for payments using Twitter as a service.
  • Pseudonym bridges. See e.g this tweet. That might be an approach to blend identity/credibility with the ability to tweet and speak more freely.
DAO 
  • Why not set up a DAO to control a Twitter board seat? The DAO could be part of fundraising to do a Twitter buy out, or just to add an interesting twist to governance. The DAO could be constrained in terms of who can be nominated for the board seat (e.g. would need specific experiences or credentials so a shiba inu isn't elected to the board). This experiment in governance could also have the option for Twitter (or another party) to buy out and retire the DAO, or the DAO can be redeemed, if this board mechanism creates too many issues in the long run.
Lots of Other Stuff
  • There are lots of deeper integrations of web3 and crypto that could occur including some gaming uses cases, eventually rebuilding the product on web3 rails etc. However, the above are some of the obvious/easiest ones to implement...

NOTES
[1] Dan Romero of Farcaster, of course, thought about this a while ago!

Thanks to Matt Huang for quick comments on this post.

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Monday, October 11, 2021

MegaCycles in Tech & Crypto

Every 8-10 years, the technology industry used to go through a boom and bust cycle. A new technology or platform would emerge, there would be rampant investment and speculation, a few strong hypergrowth survivors would emerge and most of the rest of the new startups would collapse or get consolidated. This happened with semiconductors in the 60s & 70s, microcomputers in the 80s, and the internet in the 90s. 

Each successive wave was bigger than the prior - both in terms of market cap created as well as money that flooded in. 



In the 2000s until now, something odd happened. The venture boom and bust cycle stopped. Things in tech have been replaced by a single long ~20 year boom. Even the financial crisis of 2007-2009 did not impact tech startup formation or growth much.

While there are undoubtedly multiple drivers for this lack of tech cyclicality [1] one potential explanation is the stacking of technology waves on top of each other. Instead of a single cycle drawn out over 8-10 years for a single new technology, we are now seeing multiple overlapping tech waves all happening on top of one another. This is both increasing the size of the overall resultant result, as well as smoothing out any down cycles.

For example, from 2005 until now we have had overlapping waves of cloud, social, mobile, SaaS, vertical SaaS, fintech, AI, and crypto. All of these would have had their own 10-year cycle in the past. One could argue we are now going through a mega-cycle ("poly-cycle"?) which may last for (at least? at most?) a few more years. Part of this cycle is dependent on capital availability and quantitative easing, but a lot of it is just software eating multiple industries simultaneously. COVID was a big driver of tech adoption as well across both consumers (Instacart, DoorDash, Amazon, etc) and enterprises (Zoom, Stripe, Figma).

One of the main characteristics of a megacycle is the lack of downturns. Instead of a sharp recession in tech leading to lots of layoffs, companies dying etc, the good times just keep rolling as wave after wave of new technology shifts overlap. This has both good and bad side effects to be discussed in a future post.

The time between these cycles has been collapsing, and the size of each cycle increasing. As each wave accelerates, it also may accelerate subsequent waves. For example the 10Xing of people online, time spent online, spend online, have all accelerated each other and their underlying technology waves. Sometimes adoption of something increases more adoption due to network or scale effects, versus slows things down (although large numbers inevitably catch up).

Interestingly, crypto itself has previously had much steeper cycles on roughly 4 year cycles (timed with Bitcoin halving and therefore a sudden shift in supply/demand in crypto leading to bitcoin and then alt-coin runs). The first crypto cycle was effectively the bitcoin white paper drop + initial mining. The second cycle was the emergence of Ethereum, ICOs, and new protocols & tokens. Many crypto people I know assumed that in 2021 we should have had a down cycle in crypto.


One potential explanation for a lack of downside is the massive money printing and spending being done by the US government, which is now literally just sending people cash. This creates both monetary stimulus that will inflate multiple assets, as well as potentially have investors seek crypto as an inflationary/low of dollar value hedge.

An alternative, or overlapping explanation one could argue is around overlapping crypto cycles - or a crypto mega-cycle. Just as tech had multiple overlapping waves, crypto is now seeing the same thing with multiple overlapping waves of DeFi (Uniswap, Aave, Maker, etc.), NFTs, DAOs, and the launch of multiple interesting layer 1 protocols all started a few years ago (Solana, Near, Celo, Minna, etc.). There are also an increasing number of efforts being built between crypto and traditional fiat rails, as well as broader adoption trends. Given the diversity of crypto efforts, a sudden drop and decimation in one part of the market (for example if NFTs hit a bump for some reason) may be smoothed out by a new trend or rise in another (DeFi or BTC running). In other words, parts of the market may short-term decrease their correlation a little over time as the footprint of crypto and its uses cases expands.

Some argue Crypto may also have gotten large enough in terms of usage and market cap that the extremely large fluctuations from the past may dampen a bit. Loosing 50% of a $2.4 trillion market cap is a $1.2 trillion shift - but you still have over $1 trillion of market cap left and potentially lots of buyers in the wings due to sheer scale and multiple use cases. 

Sustainability of market cap may reenforce the reflexive nature of crypto. Even when crypto dropped 50% in 2021 it still had a $1 trillion+ market cap and it was clear it was not going away for good. This drives more participants ongoing into the market so there will be more buyers and users and the potential for a stronger snap back. Similarly, great talent may continue to enter the market in the absence of a sharp dip. In 2000-2001, there we mass layoffs in technology and many people who left tech did not come back. In 2017 talent into crypto slowed as the downcycle hit. Currently the better sustainability of crypto market cap means more tech and new grad talent continues to come into crypto which should help push the next innovation waves in the market.


It will be interesting to watch the coming year as to whether crypto goes back into cyclical behavior with a sharper downturn soon, or if we are now caught in a megacycle. 

The big confounder on this all is ongoing mass scale government money printing and spending. Given ongoing supply chain and energy issues and pricing rising in various industries, more people seem worried about the chances for transitory or prolonged inflation or "stagflation"[2]. Bitcoin was started in part as a reaction to the great financial crisis and US monetary policy. One could argue the early origins of cryptocurrencies was to hedge the exact environment we now find ourselves in[3].

Thanks to Matt Huang, Fred Ehrsam, and Curtis Spencer for comments on this post.

NOTES

[1] For example, one aspect or cause of the boom may be explained by a few things including the ongoing need for capital to find a high return / high growth home due to low to no interest rates caused by quantitative easing. So a lot more money has flown into tech over time due to a lack of yield anywhere else.

[2] Stagflation is when there is rising inflation - so companies need to pass on higher pricing to cushion margins. However those higher prices result in demand destruction and a stagnant economy.

[3] So "hedge US macro" may be sufficient cause for a flight to crypto right now and the quick rebound from a down cycle.


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Sunday, September 26, 2021

The False Narrative Around Theranos

One of the interesting aspects of the Theranos trial is the degree to which some folks are buying into part of the defense's narrative that "Theranos was just acting like every Silicon Valley startup". This is of course blatantly false, but it is being adopted as some form of truth. 

The claim is that every tech founder somehow pushes the envelope of truth, and therefore that is all Theranos did (versus potentially committing fraud over a 15 year period, lying to regulators, physicians, employees and investors while endangering patients).

This analogy breaks down on multiple levels. 

There is a big difference between drunk driving at 90 mph in a school zone versus driving 5 miles too fast on the freeway. (Or, in the case of most tech companies, simply respecting the speed limit).

While there are obviously some bad actors in tech, there does not appear to be quantitative evidence to suggest this is any worse than in non-profits, finance, media & hollywood, or any other sectors. 

Where the analogy breaks down between Theranos and the "average" tech company:

1. Most technology companies do not lie about their product or service. If they did so, they would not be able to attract or retain great employees, or scale revenue and product adoption so rapidly. If their product did not work, no one would use it. This is especially true over a decade+ journey. Despite some high profile counter-examples, most tech companies are honest companies run by people who want to do good.

2. Over its 15 year history (and $700 million raised), Theranos never had a working product. It appears possible Theranos' approach was potentially unlikely to actually be able to work based on chemistry/biological contamination of approach. Think about this for a minute. Theranos apparently lied to people about its product's potential for 15 years without ever making it work. 

Additionally, given the small titers of blood, the finger prick as a source of contaminant versus venus blood draws, and other aspects of the chemistry, some believe the Theranos approach is extremely hard to make work from a chemistry perspective.

3. The company launched a fake product to living, breathing patients whose potential course of treatment and therefore life and death situations depended on accurate results. This is different from a telling a CIO that your data science tool for their customer support team would be ready in Q1 and missing the deadline. Real harm happened to real people, who course of care depended on Theranos results.

4. The scale of lying was exceptional. Theranos appears to have misled regulators, employees, investors, partners, physicians and patients. It immersed itself in secrecy, even internally, to be able to keep employees from talking to each other and for the ongoing deceit to be detected. This is different from most tech startups where transparency is often one of its early core principles or approaches. From weekly all-hands to internal Looms, tech startups tend to be highly transparent places to work.

5. Theranos raised no real mainstream venture capital. None of the mainstream tech or biotech funders invested in Theranos. Given that there are tens of reasonably good venture firms, this is striking.

6. Theranos was a diagnostics company, not a "tech" company. It is striking how many non-tech companies that blow up did not really ever have much to do with technology. Theranos fit squarely in the "medical devices & diagnostics" world and its focus and (never-quite-worked) innovation was on the chemistry and hardware for biology side. WeWork - another company often pointed to as a "tech company", which had issues for other reasons (but started off as a viable business) was a real estate company. Branding one's own company as "tech" tends to be give it higher multiples, access to more money, and a brand allure with media. Eventually reality tends to catch up.

An increasing portion of the discourse in the USA today seems to be anti-tech, anti-maker, and anti-success. This stance probably reflects more of what is currently happening in the people writing these negative pieces (or opining in articles) rather than in the tech industry itself. Theranos is being used as a catchall example to drive this false narrative that people can not do good work, benefit millions of people, and make money, without somehow being nefarious. I encourage you to not buy into this false hype. :)

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Monday, June 21, 2021

Unicorn Market Cap, June 2021 (Almost Post-Pandemic Edition)

I have previously written about Unicorn Market Cap and Industry towns in 2019 and 2020. Over the last 8 months the number of tech startups worth $1B or more ("unicorns") has grown by 43% from 487 Unicorns to 701. This is almost double the 361 unicorns in June 2019 (!). 

Data was taken from CB Insights and a special thank you to Shin Kim, CEO of Eraser for the data and graphs. 

Caveat emptor: data from CBI is updated/reconciled over time, so very recent unicorns may not be included yet. However this provides a directional view.... Raw data here.

NEW UNICORNS

The regional nature of private tech market cap continues to dominate. The big shifts over the last year include:

NEW UNICORNS SINCE OCTOBER 2020
(1) United states: Over 67% of the new unicorns by # are in the USA with 154 total. (out of 227 globally)
There were 69 new unicorns in Silicon Valley, 30 in New York, and 8 in Los Angeles. 

This is increasing share for both the USA & Silicon Valley as a % of global tech unicorns, with NY and LA accelerating somewhat. New York anecdotally feels like it has transitioned into a break out cluster of its own.

The pandemic has increased unicorns in the USA at a fast clip.

(2) China has slowed on new unicorn generation.

While China is 29% of all unicorn market cap, it only added 9 new unicorns (roughly 4% of global total) since October 2020.

The decline in new unicorn formation in China is striking. One potential interpretation is it at least in part a data issue. For example, 20 or so Chinese unicorns from pre-2020 were just added to this data set as a historical reconciliation. Other interpretation in the last section below.

(3) Europe added 25 unicorns.
London (8 new unicorns for a total of 21), Paris (5 new unicorns for a total of 13), Berlin (5 new unicorns for a total of 9), and Stockholm (2 new unicorns for a total of 4, including a decacorn) added the most new unicorns. 

(4) India added 11 unicorns.
Major cities to add unicorns included Bangalore (7 new unicorns for a total of 14), New Delhi (2 new for a total of 12), Mumbai (1 new unicorn for a total of 4), and Chennai (1).  

OVERALL CONCENTRATION

The overall Unicorn rankings have remained the same. The USA is the clear front-runner, China next, followed by the EU and then India. Israel continues to have a large number of unicorns per capita and Canada has started adding them at a faster clip then before (4 new unicorns in Toronto alone in the last 10 months). Brazil is the biggest generator of unicorns in Latin America.



Unicorn market cap is highly concentrated by specific cities:


DECACORNS: $10B or larger market caps
Decacorns are largely concentrated in a handful of countries - although a number of decacorns have gone public in e.g. Indonesia and others.



UNICORN REGION AND CITY CONCENTRATION
Within each country, specific cities or regions continue to make up 50% or more of the country unicorn market cap.


For example, Silicon Valley is 51% of the US market cap and 47% of unicorns by number. New York is 11% of market cap and 17% of unicorn by number, and Los Angeles is 11% of market cap (largely due to SpaceX) and 7% of US unicorns by #.

Clusters & industry towns clearly continue to matter.

Europe has a number of centers with London the largest by a margin, followed by Stockholm, Paris, and Berlin.

Beijing (59% of market cap due to ByteDance and 37% of # of Chinese unicorns) and Shanghai (13% of market cap and 24% of unicorns) are the largest in China.


LOTS OF CITIES HAVE 1 UNICORN
While 77 cities around the world have at least 1 unicorn, most are concentrated in 13 cities with 11 or more unicorns.



USA CLUSTERS:
"New tech clusters" are not big clusters yet
A lot of great marketing has occurred for Austin and Miami during the pandemic. Austin added 3 unicorns to get to 5 total (below new additions of unicorns in SV, NY, LA, Chicago and Boston and tied with Seattle, DC, Philadelphia). Miami added 1 unicorn to get to a total of 3 (although I am aware of at least one other that just moved there from LA that does not appear counted in the data yet). I am anecdotally bullish on the long term prospects of Miami given the people I know who moved there and the frontier feeling it has. Under discussed as future clusters with strong prospects are Denver (2 new unicorns for a total of 4) & Boulder (1 new unicorn) and Salt Lake City (2 new unicorns for a total of 4). 

As noted above, Silicon Valley continues to make up 51% of market cap and 47% of total unicorns in the USA. NY has grown its share of unicorns by number relative to other US cities.


Here is the full list for the USA


CHINA CLUSTERS
Beijing, Shanghai, and to a lessor extent Shenzen continue to drive unicorn # and market cap in China.

INDIA
In India Bangalore and New Delhi continue to run neck in neck, with Bangalore having 2 more unicorns.

EUROPE (and Israel)
Europe continues to see strong clusters in the UK (where London easily dominates), Germany (ditto for Berlin) and France (ditto for Paris).

UK:




Germany:



In Israel, Tel Aviv continues to be the main cluster.

UNICORN GRADUATION
In parallel to new unicorn formation, unicorns also "graduate" via an IPO/DL/SPAC, an acquisition, or going out of business. 
As unicorns graduate the founders and employees of the companies may either fund other new ventures as angels or VCs, or start new companies themselves. It will be exciting to watch more tech ecosystems grow as more unicorns go public.

VELOCITY OF NEW UNICORNS
This data should be caveated as new unicorns from past years are sometimes added by CB Insights (who have done a great public service by aggregating this data to begin with!).

So caveat emptor on over interpreting this data. However, the difference between the USA and China on new tech unicorns over the last 2 years in notable.

Possible interpretation would include:
1. Data fidelity. Is China reporting delayed?

2. Keeping it quiet. Given the unusually high death rate (a Chinese billionaire dies every 40 days) and kidnappings of Chinese billionaires, perhaps there is now a disincentive to announce highly valued financing rounds? 眼不见,心为静。 眼不见心不烦。

3. China is producing fewer tech unicorns. This seems odd given the acceleration seen in tech due to the pandemic, but is possible. 

Some of the top countries for rest of world look like this:



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